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  11. Prediction Of Tumor-associated Macrophages And Immunotherapy Benefits Using Weakly Supervised Contrastive Learning In Breast Cancer Pathology Images.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Prediction Of Tumor-associated Macrophages And Immunotherapy Benefits Using Weakly Supervised Contrastive Learning In Breast Cancer Pathology Images.

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Prediction of Tumor-Associated Macrophages and Immunotherapy Benefits Using Weakly Supervised Contrastive Learning in Breast Cancer Pathology Images.

Guobang Yu1, Yi Zuo1, Bin Wang2

  • 1College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China.

Journal of Imaging Informatics in Medicine
|June 17, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Digital pathology images can predict immunotherapy response by analyzing tumor-infiltrating immune cells, particularly macrophages, using advanced AI. This offers a cost-effective alternative to RNA sequencing for routine clinical use.

Keywords:
Biomarker geneComputational histopathologyContrastive learningImmunotherapy

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Artificial intelligence in oncology

Background:

  • Tumor immune microenvironment (TIME) influences immunotherapy efficacy.
  • RNA sequencing for TIME analysis is costly and time-consuming.
  • Pathology images may infer TIME characteristics, but quantitative immune cell estimation is underexplored.

Purpose of the Study:

  • To develop a computational method using whole slide images (WSIs) to infer tumor-associated macrophages and immunotherapy benefit.
  • To establish a link between histological morphology and cellular composition for clinical applications.

Main Methods:

  • Integrated contrastive learning and weakly supervised learning on H&E stained WSIs.
  • Extracted tile-level features using contrastive learning and aggregated them.
Macrophages
Tumor immune microenvironment
Whole slide image
  • Fine-tuned the encoder using weak supervisory signals for downstream tasks.

    • Main Results:

    • Accurately predicted tumor-infiltrating immune cell proportions, especially macrophages.
    • Successfully identified immune subtypes and potential immunotherapy benefit.
    • Demonstrated model's effectiveness on independent breast cancer cohorts and spatial transcriptomics data.

    Conclusions:

    • Computational pathology features from WSIs can accurately predict immune cell infiltration and immunotherapy response.
    • The model captures pathological features beyond human vision, linking histology to cellular composition.
    • This approach expands the clinical utility of digital pathology images for cancer treatment decisions.